The present application is directed to modeling systems which include components and connections between the components. More particularly, the application is concerned with the modeling of connections to enable identification of faults in the connections. In one embodiment such modeling may be accomplished by extending model-based diagnosis (MBD) to include reasoning about connection faults.
Components may be any of a number of objects depending on the particular system, e.g., circuit gates, transistors, pumps and motors, among others. Connections are the pathways between the components, including wires, pipes and shafts, to name a few. In the following discussion, a particular point of connection may be identified in this description as a node. Therefore, at times the words connection and node are used interchangeably herein.
Almost all approaches to diagnosis, both model-based and not, presume connections between system components are ideal. Unfortunately, connections often fail, and they fail in unforeseen ways. Two wires can short together, creating new circuit behavior. A pipe can leak such that there is a significant pressure drop where none was ever expected.
Early work on model-based diagnosis addressed bridge faults in systems. However, this early research treats shorts as a special case, hypothesizing bridge faults only when all single faults were eliminated. Another modeling approach which attempts to model shorts inserts additional insulating components at places where shorts may occur and uses stable-model semantics to identify candidate diagnoses. This approach is inefficient, as the number of possible insulator components to consider grows quadratically with system size. Still another procedure models structural shorts in analog systems, using the possibility of multiple faults to invoke an additional algorithm to match observed behavior to known hidden interaction models.
Other modeling techniques come from qualitative reasoning (QR) and model based diagnosis (MBD) work in automotive diagnosis and the failure modes and effects analysis (FMEA) construction domains. One methodology proposed in this area is to use multiple variables to represent wires.
As existing diagnostic reasoning approaches presume connections to be ideal most of these approaches model components but not their connections. For example, artificial intelligence (AI) diagnostic reasoning, for digital systems, presume that digital components can be modeled as pure functions of their inputs, all signals can be represented by “1”s and “0”s, wires between components cannot fail, and therefore do not model replacement of components or wires.
Even those modeling approaches that try to model connections try to model the possibility a connection can break, not that two connections may join, e.g., through fluid leakage or electrical short circuit. Among the reasons for this are that modeling these connection failures could require an exponential number of connection failure possibilities, and that modeling connection failures requires modeling the system at a level of precision which requires far more complex models.
None of the above assumptions are valid for the challenges diagnosticians encounter in real world systems. Also, although digital systems can be modeled at the analog level with programs such as SPICE or, at the digital/analog level, with VHDL-based simulators, they are not designed for diagnostic use, require accurate hard-to-obtain component models and do not present results in a way a human diagnostician can understand.